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| #!/usr/bin/env python3 | |
| import os | |
| import sys | |
| import subprocess | |
| import io | |
| import json | |
| import math | |
| from pathlib import Path | |
| from PIL import Image, ImageDraw, ImageFilter |
| name | cognitive-rhythm-writing |
|---|---|
| description | 説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替（観察→逡巡→断定→再観察）と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。 |
密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード（観察する、迷う、確信する、確かめ直す）を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
This guide was created to solve TLS certificate verification issues when installing Poste.io on Dokploy, using Traefik as the reverse proxy.
The goal is to prevent conflicts between Traefik and Poste.io during certificate validation and ensure that all mail services (SMTP, IMAP, POP3) work correctly with valid TLS certificates — not just the web UI.
Before going any further, I want to be very clear: this solution is not originally mine.
| { | |
| "agent_id": "cashvertising_v5", | |
| "execution_mode": { | |
| "invocation_type": "always_parallel", | |
| "description": "This agent is always invoked in parallel with all other copy agents. Specializes in biological-desire-first persuasion architecture (LF8) and consumer psychology sequencing.", | |
| "expected_latency": "same as other parallel agents — no priority queue", | |
| "output_label": "cashvertising_ad", | |
| "orchestrator_note": "Optimized for long-form direct response (sales pages, VSLs, email sequences, advertorials). For formats under 200 words: LF8 entry + single PVA scene + one CTA is sufficient. Full 12-phase protocol not required for micro-formats." | |
| }, | |
| "agentselectorbrief": { |
| #version 300 es | |
| #define varying in | |
| out highp vec4 pc_fragColor; | |
| #define gl_FragColor pc_fragColor | |
| #define gl_FragDepthEXT gl_FragDepth | |
| #define texture2D texture | |
| #define textureCube texture | |
| #define texture2DProj textureProj | |
| #define texture2DLodEXT textureLod | |
| #define texture2DProjLodEXT textureProjLod |